ArticleSensors (Basel, Switzerland)2025
Detection of Anomalies in Data Streams Using the LSTM-CNN Model.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A metaheuristic feature selection model using bat optimization for malicious URL attack detection.Scientific reports · 2026Article
- Fractional-order neural network for detecting process deviations in optical fiber cable manufacturing.Scientific reports · 2026Article
- Traffic flow prediction via dynamic hypergraph learning.PloS one · 2026Article
- POC Sensor Systems and Artificial Intelligence-Where We Are Now and Where We Are Going?Biosensors · 2025Review
- Leveraging retinanet based object detection model for assisting visually impaired individuals with metaheuristic optimization algorithm.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the literature, which are the basis for this analysis, are the following: the LSTM and its more complicated variant, the LSTM autoencoder. Additionally, the usefulness of an innovative LSTM-CNN approach is evaluated. The results indicate that the LSTM-CNN approach can successfully be applied for anomaly detection in data streams as its performance compares favorably with that of the two mentioned standard models. For the performance evaluation, the F1score is used.
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